General Sparse Matrix-Matrix Multiplication (SpGEMM) has attracted much attention from
researchers in graph analyzing, scientific computing, and deep learning. Many optimization …

High-performance implementations of graph algorithms are challenging to implement on
new parallel hardware such as GPUs because of three challenges:(1) the difficulty of coming …

Sparse general matrix-matrix multiplication (SpGEMM) is one of the most fundamental
building blocks in sparse linear solvers, graph processing frameworks and machine learning …

Sparse solvers provide essential functionality for a wide variety of scientific applications.
Highly parallel sparse solvers are essential for continuing advances in high-fidelity, multi …

Exact triangle counting algorithm enumerates the triangles in a graph by identifying the
common neighbors of two vertices of each edge. In this work, we present TriCore, a scalable …

Graph pattern mining (GPM) is a class of algorithms widely used in many real-world
applications in bio-medicine, e-commerce, security, social sciences, etc. GPM is a …

When considering uncertain bipartite networks, the number of instances of the popular
graphlet structure the butterfly may be used as an important metric to quickly gauge …

SuiteSparse: GraphBLAS is a full implementation of the GraphBLAS standard, which defines
a set of sparse matrix operations on an extended algebra of semirings using an almost …

Graph pattern mining (GPM) is a key building block in diverse applications, including
bioinformatics, chemical engineering, social network analysis, recommender systems and …

Graph Pattern Mining (GPM) extracts higher-order information in a large graph by searching
for small patterns of interest. GPM applications are computationally expensive, and thus …